from sklearn.datasets import load_files       
from tensorflow.keras.utils import to_categorical
import numpy as np
from glob import glob

# Define function to load train, test, and validation datasets
def load_dataset(path):
    data = load_files(path)
    dog_files = np.array(data['filenames'])
    dog_targets = to_categorical(np.array(data['target']), 133)
    return dog_files, dog_targets

# Load train, test, and validation datasets
train_files, train_targets = load_dataset('data/dog_images/train')
valid_files, valid_targets = load_dataset('data/dog_images/valid')
test_files, test_targets = load_dataset('data/dog_images/test')

# Load list of dog names
dog_names = [item[20:-1] for item in sorted(glob("data/dog_images/train/*/"))]

# Print statistics about the dataset
print('There are %d total dog categories.' % len(dog_names))
print('There are %s total dog images.\n' % len(np.hstack([train_files, valid_files, test_files])))
print('There are %d training dog images.' % len(train_files))
print('There are %d validation dog images.' % len(valid_files))
print('There are %d test dog images.' % len(test_files))

There are 133 total dog categories.
There are 8351 total dog images.

There are 6680 training dog images.
There are 835 validation dog images.
There are 836 test dog images.

import random
random.seed(8675309)

# load filenames in shuffled human dataset
human_files = np.array(glob("data/lfw/*/*"))
random.shuffle(human_files)

# print statistics about the dataset
print('There are %d total human images.' % len(human_files))

There are 13233 total human images.

Data Exploration

import pandas as pd
import matplotlib.pyplot as plt

dog_names_clean = [name[6:].lower().replace("_"," ") for name in dog_names]
train_df = pd.DataFrame(data=train_targets, columns=dog_names_clean)
valid_df = pd.DataFrame(data=valid_targets, columns=dog_names_clean)
test_df = pd.DataFrame(data=test_targets, columns=dog_names_clean)

# total graph


fig, ax = plt.subplots(1,4,figsize=(10,133//5))

total_df = (train_df.sum() + valid_df.sum() + test_df.sum()).sort_values()
values = list(total_df)
x = total_df.index

ax[0].barh(range(len(x)), sorted(values), align='center', color='black', ecolor='black')
ax[0].set_yticks(ticks=range(len(x)))
ax[0].set_yticklabels(x)
ax[0].set_ylim(0, 133)
ax[0].set_title("Total", fontsize=15)

train_count_df = train_df.sum()
values = list(train_count_df)
x = train_count_df.index

ax[1].barh(range(len(x)), sorted(values), align='center', color='green', ecolor='black')
ax[1].set_yticks(ticks=[])
ax[1].set_ylim(0, 133)
ax[1].set_xlim(0,100)
ax[1].set_title("Train", fontsize=15)

valid_count_df = valid_df.sum()
values = list(valid_count_df)
x = valid_count_df.index

ax[2].barh(range(len(x)), sorted(values), align='center', color='red', ecolor='black')
ax[2].set_yticks(ticks=[])
ax[2].set_ylim(0, 133)
ax[2].set_xlim(0,100)
ax[2].set_title("Valid", fontsize=15)

test_count_df = test_df.sum()
values = list(test_count_df)
x = test_count_df.index

ax[3].barh(range(len(x)), sorted(values), align='center', color='blue', ecolor='black')
ax[3].set_yticks(ticks=[])
ax[3].set_ylim(0, 133)
ax[3].set_xlim(0,100)
ax[3].set_title("Test", fontsize=15)

Text(0.5, 1.0, 'Test')

---

Step 1: Detect Humans

We use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images. OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the haarcascades directory.

In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.

import cv2                
import matplotlib.pyplot as plt                        
%matplotlib inline                               

# extract pre-trained face detector
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')

# load color (BGR) image
img = cv2.imread(human_files[3])
# convert BGR image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# find faces in image
faces = face_cascade.detectMultiScale(gray)

# print number of faces detected in the image
print('Number of faces detected:', len(faces))

# get bounding box for each detected face
for (x,y,w,h) in faces:
    # add bounding box to color image
    cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
    
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# display the image, along with bounding box
plt.imshow(cv_rgb)
plt.show()

Number of faces detected: 1

Before using any of the face detectors, it is standard procedure to convert the images to grayscale. The detectMultiScale function executes the classifier stored in face_cascade and takes the grayscale image as a parameter.

In the above code, faces is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x and y) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w and h) specify the width and height of the box.

Write a Human Face Detector

We can use this procedure to write a function that returns True if a human face is detected in an image and False otherwise. This function, aptly named face_detector, takes a string-valued file path to an image as input and appears in the code block below.

# returns "True" if face is detected in image stored at img_path
def face_detector(img_path):
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray)
    return len(faces) > 0

(IMPLEMENTATION) Assess the Human Face Detector

Question 1: Use the code cell below to test the performance of the face_detector function.

• What percentage of the first 100 images in human_files have a detected human face?
• What percentage of the first 100 images in dog_files have a detected human face?

Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face. You will see that our algorithm falls short of this goal, but still gives acceptable performance. We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays human_files_short and dog_files_short.

Answer:

human_files_short = human_files[:100]
dog_files_short = train_files[:100]
# Do NOT modify the code above this line.

## TODO: Test the performance of the face_detector algorithm 
## on the images in human_files_short and dog_files_short.

detected_faces_human = 0
for human in human_files_short:
    if face_detector(human):
        detected_faces_human += 1
        
detected_faces_dog = 0
for dog in dog_files_short:
    if face_detector(dog):
        detected_faces_dog += 1
        
print(f"detected faces in {detected_faces_human}% of pictures in human dataset\n")
print(f"detected faces in {detected_faces_dog}% of pictures in dog dataset")

detected faces in 99% of pictures in human dataset

detected faces in 12% of pictures in dog dataset

---

Dog classifier using pretraned model ResNet-50

In this section, we use a pre-trained ResNet-50 model to detect dogs in images. Our first line of code downloads the ResNet-50 model, along with weights that have been trained on ImageNet, a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of 1000 categories. Given an image, this pre-trained ResNet-50 model returns a prediction (derived from the available categories in ImageNet) for the object that is contained in the image.

from keras.applications.resnet50 import ResNet50

# define ResNet50 model
ResNet50_model = ResNet50(weights='imagenet')

Pre-process the Data

When using TensorFlow as backend, Keras CNNs require a 4D array (which we'll also refer to as a 4D tensor) as input, with shape

$$ (\text{nb_samples}, \text{rows}, \text{columns}, \text{channels}), $$

where nb_samples corresponds to the total number of images (or samples), and rows, columns, and channels correspond to the number of rows, columns, and channels for each image, respectively.

The path_to_tensor function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN. The function first loads the image and resizes it to a square image that is $224 \times 224$ pixels. Next, the image is converted to an array, which is then resized to a 4D tensor. In this case, since we are working with color images, each image has three channels. Likewise, since we are processing a single image (or sample), the returned tensor will always have shape

$$ (1, 224, 224, 3). $$

The paths_to_tensor function takes a numpy array of string-valued image paths as input and returns a 4D tensor with shape

$$ (\text{nb_samples}, 224, 224, 3). $$

Here, nb_samples is the number of samples, or number of images, in the supplied array of image paths. It is best to think of nb_samples as the number of 3D tensors (where each 3D tensor corresponds to a different image) in your dataset!

from keras.preprocessing import image                  
from tqdm import tqdm

def path_to_tensor(img_path):
    # loads RGB image as PIL.Image.Image type
    img = image.load_img(img_path, target_size=(224, 224))
    # convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)
    x = image.img_to_array(img)
    # convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor
    return np.expand_dims(x, axis=0)

def paths_to_tensor(img_paths):
    list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]
    return np.vstack(list_of_tensors)

path_to_tensor("images/selfie.jpeg").shape

(1, 224, 224, 3)

Making Predictions with ResNet-50

Getting the 4D tensor ready for ResNet-50, and for any other pre-trained model in Keras, requires some additional processing. First, the RGB image is converted to BGR by reordering the channels. All pre-trained models have the additional normalization step that the mean pixel (expressed in RGB as $[103.939, 116.779, 123.68]$ and calculated from all pixels in all images in ImageNet) must be subtracted from every pixel in each image. This is implemented in the imported function preprocess_input. If you're curious, you can check the code for preprocess_input here.

Now that we have a way to format our image for supplying to ResNet-50, we are now ready to use the model to extract the predictions. This is accomplished with the predict method, which returns an array whose $i$-th entry is the model's predicted probability that the image belongs to the $i$-th ImageNet category. This is implemented in the ResNet50_predict_labels function below.

By taking the argmax of the predicted probability vector, we obtain an integer corresponding to the model's predicted object class, which we can identify with an object category through the use of this dictionary.

from keras.applications.resnet50 import preprocess_input, decode_predictions

def ResNet50_predict_labels(img_path):
    # returns prediction vector for image located at img_path
    img = preprocess_input(path_to_tensor(img_path))
    return np.argmax(ResNet50_model.predict(img))

Write a Dog Detector

While looking at the dictionary, you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from 'Chihuahua' to 'Mexican hairless'. Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained ResNet-50 model, we need only check if the ResNet50_predict_labels function above returns a value between 151 and 268 (inclusive).

We use these ideas to complete the dog_detector function below, which returns True if a dog is detected in an image (and False if not).

### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
    prediction = ResNet50_predict_labels(img_path)
    return ((prediction <= 268) & (prediction >= 151)) 

(IMPLEMENTATION) Assess the Dog Detector

Question 3: Use the code cell below to test the performance of your dog_detector function.

• What percentage of the images in human_files_short have a detected dog?
• What percentage of the images in dog_files_short have a detected dog?

Answer:

# Dog detection in Both Sets

dogs_in_human_dataset = 0
for human in human_files_short:
    if dog_detector(human):
        dogs_in_human_dataset += 1

dogs_in_dog_dataset = 0
for dog in dog_files_short:
    if dog_detector(dog):
        dogs_in_dog_dataset += 1
        
print(f"dogs found in {dogs_in_human_dataset}% of pictures in human dataset")
print(f"dogs found in {dogs_in_dog_dataset}% of picttures in dog datase")

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dogs found in 0% of pictures in human dataset
dogs found in 100% of picttures in dog datase

---

Step 3: Create a CNN to Classify Dog Breeds (from Scratch)

Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, you will create a CNN that classifies dog breeds. You must create your CNN from scratch (so, you can't use transfer learning yet!), and you must attain a test accuracy of at least 1%. In Step 5 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.

Be careful with adding too many trainable layers! More parameters means longer training, which means you are more likely to need a GPU to accelerate the training process. Thankfully, Keras provides a handy estimate of the time that each epoch is likely to take; you can extrapolate this estimate to figure out how long it will take for your algorithm to train.

We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that even a human would have great difficulty in distinguishing between a Brittany and a Welsh Springer Spaniel.

We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.

Remember that the practice is far ahead of the theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun!

Pre-process the Data

We rescale the images by dividing every pixel in every image by 255.

from PIL import ImageFile                            
ImageFile.LOAD_TRUNCATED_IMAGES = True                 

# pre-process the data for Keras
train_tensors = paths_to_tensor(train_files).astype('float32')/255
valid_tensors = paths_to_tensor(valid_files).astype('float32')/255
test_tensors = paths_to_tensor(test_files).astype('float32')/255

100%|█████████████████████████████████████████████████████████████████████████████| 6680/6680 [00:28<00:00, 236.70it/s]
100%|███████████████████████████████████████████████████████████████████████████████| 835/835 [00:04<00:00, 176.49it/s]
100%|███████████████████████████████████████████████████████████████████████████████| 836/836 [00:03<00:00, 217.65it/s]

(IMPLEMENTATION) Model Architecture

Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:

    model.summary()

We have imported some Python modules to get you started, but feel free to import as many modules as you need. If you end up getting stuck, here's a hint that specifies a model that trains relatively fast on CPU and attains >1% test accuracy in 5 epochs:

Question 4: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. If you chose to use the hinted architecture above, describe why you think that CNN architecture should work well for the image classification task.

Answer: I added 2 zero padding followed by convolution 2d layers - that way the we have 2 convolution layers without loosing input size. I then added another convolution 2d + maxpooling2d layer, which reduces by half the size of the input. Then, i used a flatten layer to convert the input into a 1d array, and added dense with ReLu activation, dropout with 0.2 probability and dense with softmax activation layers to produce the final output.

from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, ZeroPadding2D
from keras.layers import Dropout, Flatten, Dense
from keras.models import Sequential

model=Sequential()

model.add(ZeroPadding2D((1,1),input_shape=(224,224,3)))
model.add(Conv2D(32,kernel_size=(3,3),activation='relu'))
model.add(ZeroPadding2D(padding=(1,1)))
model.add(Conv2D(32,kernel_size=(3,3),activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))

model.add(Flatten())
model.add(Dense(64,activation='relu'))
model.add(Dropout(0.2))

model.add(Dense(133,activation='softmax'))

# model.compile(loss=categorical_crossentropy,optimizer='adam',metrics=['accuracy'])
model.summary()

C:\Users\ACER\anaconda3\Lib\site-packages\keras\src\layers\reshaping\zero_padding2d.py:72: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(**kwargs)

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ zero_padding2d (ZeroPadding2D)       │ (None, 226, 226, 3)         │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ conv2d (Conv2D)                      │ (None, 224, 224, 32)        │             896 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ zero_padding2d_1 (ZeroPadding2D)     │ (None, 226, 226, 32)        │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ conv2d_1 (Conv2D)                    │ (None, 224, 224, 32)        │           9,248 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ max_pooling2d (MaxPooling2D)         │ (None, 112, 112, 32)        │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ flatten (Flatten)                    │ (None, 401408)              │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense (Dense)                        │ (None, 64)                  │      25,690,176 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout (Dropout)                    │ (None, 64)                  │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_1 (Dense)                      │ (None, 133)                 │           8,645 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 25,708,965 (98.07 MB)

 Trainable params: 25,708,965 (98.07 MB)

 Non-trainable params: 0 (0.00 B)

Compile the Model

model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

### (IMPLEMENTATION) Train the Model Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss. You are welcome to [augment the training data](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html), but this is not a requirement.

from keras.callbacks import ModelCheckpoint  

### TODO: specify the number of epochs that you would like to use to train the model.

epochs = 5

### Do NOT modify the code below this line.

checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_scratch.keras', 
                               verbose=1, save_best_only=True)

model.fit(train_tensors, train_targets, 
          validation_data=(valid_tensors, valid_targets),
          epochs=epochs, batch_size=20, callbacks=[checkpointer], verbose=1)

Epoch 1/5
334/334 ━━━━━━━━━━━━━━━━━━━━ 0s 1s/step - accuracy: 0.0075 - loss: 6.7748
Epoch 1: val_loss improved from inf to 4.88352, saving model to saved_models/weights.best.from_scratch.keras
334/334 ━━━━━━━━━━━━━━━━━━━━ 367s 1s/step - accuracy: 0.0075 - loss: 6.7703 - val_accuracy: 0.0096 - val_loss: 4.8835
Epoch 2/5
334/334 ━━━━━━━━━━━━━━━━━━━━ 0s 1s/step - accuracy: 0.0106 - loss: 4.8910
Epoch 2: val_loss did not improve from 4.88352
334/334 ━━━━━━━━━━━━━━━━━━━━ 355s 1s/step - accuracy: 0.0106 - loss: 4.8910 - val_accuracy: 0.0144 - val_loss: 4.9100
Epoch 3/5
334/334 ━━━━━━━━━━━━━━━━━━━━ 0s 1s/step - accuracy: 0.0209 - loss: 4.7743
Epoch 3: val_loss improved from 4.88352 to 4.77955, saving model to saved_models/weights.best.from_scratch.keras
334/334 ━━━━━━━━━━━━━━━━━━━━ 353s 1s/step - accuracy: 0.0209 - loss: 4.7742 - val_accuracy: 0.0156 - val_loss: 4.7796
Epoch 4/5
334/334 ━━━━━━━━━━━━━━━━━━━━ 0s 1s/step - accuracy: 0.0673 - loss: 4.3126
Epoch 4: val_loss did not improve from 4.77955
334/334 ━━━━━━━━━━━━━━━━━━━━ 354s 1s/step - accuracy: 0.0673 - loss: 4.3125 - val_accuracy: 0.0263 - val_loss: 4.8184
Epoch 5/5
334/334 ━━━━━━━━━━━━━━━━━━━━ 0s 1s/step - accuracy: 0.2095 - loss: 3.3389
Epoch 5: val_loss did not improve from 4.77955
334/334 ━━━━━━━━━━━━━━━━━━━━ 352s 1s/step - accuracy: 0.2095 - loss: 3.3388 - val_accuracy: 0.0335 - val_loss: 5.2730

<keras.src.callbacks.history.History at 0x18c4ce8b6d0>

Load the Model with the Best Validation Loss

model.load_weights('saved_models/weights.best.from_scratch.keras')

Test the Model

Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 1%.

# get index of predicted dog breed for each image in test set
dog_breed_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]

# report test accuracy
test_accuracy = 100*np.sum(np.array(dog_breed_predictions)==np.argmax(test_targets, axis=1))/len(dog_breed_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)

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Test accuracy: 1.6746%

---

Step 4: Use a CNN to Classify Dog Breeds

To reduce training time without sacrificing accuracy, we show you how to train a CNN using transfer learning. In the following step, you will get a chance to use transfer learning to train your own CNN.

Obtain Bottleneck Features

bottleneck_features = np.load('bottleneck_features/DogResnet50Data.npz')
train_DogResnet50 = bottleneck_features['train']
valid_DogResnet50 = bottleneck_features['valid']
test_DogResnet50 = bottleneck_features['test']

Model Architecture

The model uses the the pre-trained VGG-16 model as a fixed feature extractor, where the last convolutional output of VGG-16 is fed as input to our model. We only add a global average pooling layer and a fully connected layer, where the latter contains one node for each dog category and is equipped with a softmax.

DogResnet50_model = Sequential()

DogResnet50_model.add(GlobalAveragePooling2D(input_shape=train_DogResnet50.shape[1:]))
DogResnet50_model.add(Dense(133,activation='softmax'))

DogResnet50_model.summary()

C:\Users\ACER\anaconda3\Lib\site-packages\keras\src\layers\pooling\base_global_pooling.py:12: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(**kwargs)

Model: "sequential_1"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ global_average_pooling2d             │ (None, 2048)                │               0 │
│ (GlobalAveragePooling2D)             │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_2 (Dense)                      │ (None, 133)                 │         272,517 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 272,517 (1.04 MB)

 Trainable params: 272,517 (1.04 MB)

 Non-trainable params: 0 (0.00 B)

Compile the Model

DogResnet50_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

Train the Model

### TODO: Train the model.

checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.DogResnet50.keras', 
                               verbose=1, save_best_only=True)

DogResnet50_model.fit(train_DogResnet50, train_targets, 
          validation_data=(valid_DogResnet50, valid_targets),
          epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)

DogResnet50_model.load_weights('saved_models/weights.best.DogResnet50.keras')

### TODO: Calculate classification accuracy on the test dataset.
#make predictions 
DogResnet50_predictions = [np.argmax(DogResnet50_model.predict(np.expand_dims(feature, axis=0))) for feature in test_DogResnet50]

# report test accuracy
test_accuracy = 100*np.sum(np.array(DogResnet50_predictions)==np.argmax(test_targets, axis=1))/len(DogResnet50_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)

Epoch 1/20
332/334 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.4231 - loss: 2.7189
Epoch 1: val_loss improved from inf to 0.86823, saving model to saved_models/weights.best.DogResnet50.keras
334/334 ━━━━━━━━━━━━━━━━━━━━ 2s 4ms/step - accuracy: 0.4247 - loss: 2.7090 - val_accuracy: 0.7353 - val_loss: 0.8682
Epoch 2/20
329/334 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.8658 - loss: 0.4452
Epoch 2: val_loss improved from 0.86823 to 0.67924, saving model to saved_models/weights.best.DogResnet50.keras
334/334 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 0.8658 - loss: 0.4450 - val_accuracy: 0.7892 - val_loss: 0.6792
Epoch 3/20
326/334 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.9272 - loss: 0.2357
Epoch 3: val_loss improved from 0.67924 to 0.67706, saving model to saved_models/weights.best.DogResnet50.keras
334/334 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 0.9270 - loss: 0.2363 - val_accuracy: 0.8060 - val_loss: 0.6771
Epoch 4/20
330/334 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.9552 - loss: 0.1484
Epoch 4: val_loss improved from 0.67706 to 0.62583, saving model to saved_models/weights.best.DogResnet50.keras
334/334 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 0.9551 - loss: 0.1487 - val_accuracy: 0.8192 - val_loss: 0.6258
Epoch 5/20
322/334 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.9696 - loss: 0.1061
Epoch 5: val_loss did not improve from 0.62583
334/334 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 0.9694 - loss: 0.1065 - val_accuracy: 0.8168 - val_loss: 0.6355
Epoch 6/20
329/334 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.9780 - loss: 0.0733
Epoch 6: val_loss improved from 0.62583 to 0.62221, saving model to saved_models/weights.best.DogResnet50.keras
334/334 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 0.9780 - loss: 0.0734 - val_accuracy: 0.8192 - val_loss: 0.6222
Epoch 7/20
319/334 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.9870 - loss: 0.0455
Epoch 7: val_loss did not improve from 0.62221
334/334 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 0.9869 - loss: 0.0460 - val_accuracy: 0.8096 - val_loss: 0.6857
Epoch 8/20
328/334 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.9935 - loss: 0.0335
Epoch 8: val_loss did not improve from 0.62221
334/334 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 0.9934 - loss: 0.0336 - val_accuracy: 0.8383 - val_loss: 0.6350
Epoch 9/20
321/334 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.9951 - loss: 0.0239
Epoch 9: val_loss did not improve from 0.62221
334/334 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 0.9950 - loss: 0.0241 - val_accuracy: 0.8228 - val_loss: 0.6742
Epoch 10/20
331/334 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.9964 - loss: 0.0199
Epoch 10: val_loss improved from 0.62221 to 0.61686, saving model to saved_models/weights.best.DogResnet50.keras
334/334 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 0.9964 - loss: 0.0199 - val_accuracy: 0.8287 - val_loss: 0.6169
Epoch 11/20
334/334 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.9970 - loss: 0.0131
Epoch 11: val_loss did not improve from 0.61686
334/334 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 0.9970 - loss: 0.0131 - val_accuracy: 0.8383 - val_loss: 0.6328
Epoch 12/20
328/334 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.9976 - loss: 0.0120
Epoch 12: val_loss did not improve from 0.61686
334/334 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 0.9976 - loss: 0.0120 - val_accuracy: 0.8192 - val_loss: 0.6513
Epoch 13/20
329/334 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.9988 - loss: 0.0076
Epoch 13: val_loss did not improve from 0.61686
334/334 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 0.9988 - loss: 0.0077 - val_accuracy: 0.8347 - val_loss: 0.6547
Epoch 14/20
326/334 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.9984 - loss: 0.0072
Epoch 14: val_loss did not improve from 0.61686
334/334 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - accuracy: 0.9984 - loss: 0.0072 - val_accuracy: 0.8335 - val_loss: 0.6653
Epoch 15/20
331/334 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.9984 - loss: 0.0069
Epoch 15: val_loss did not improve from 0.61686
334/334 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 0.9984 - loss: 0.0069 - val_accuracy: 0.8299 - val_loss: 0.6846
Epoch 16/20
318/334 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.9983 - loss: 0.0047
Epoch 16: val_loss did not improve from 0.61686
334/334 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 0.9983 - loss: 0.0048 - val_accuracy: 0.8347 - val_loss: 0.6675
Epoch 17/20
320/334 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.9993 - loss: 0.0042
Epoch 17: val_loss did not improve from 0.61686
334/334 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 0.9993 - loss: 0.0043 - val_accuracy: 0.8347 - val_loss: 0.6437
Epoch 18/20
318/334 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.9995 - loss: 0.0027
Epoch 18: val_loss did not improve from 0.61686
334/334 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 0.9994 - loss: 0.0028 - val_accuracy: 0.8419 - val_loss: 0.7017
Epoch 19/20
329/334 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.9991 - loss: 0.0031
Epoch 19: val_loss did not improve from 0.61686
334/334 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 0.9991 - loss: 0.0031 - val_accuracy: 0.8359 - val_loss: 0.6620
Epoch 20/20
321/334 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.9991 - loss: 0.0037
Epoch 20: val_loss did not improve from 0.61686
334/334 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 0.9990 - loss: 0.0037 - val_accuracy: 0.8395 - val_loss: 0.6936
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Test accuracy: 83.0144%

DogResnet50_model.save('saved_models/DogResnet50_model.keras')

Load the Model with the Best Validation Loss

DogResnet50_model.load_weights('saved_models/weights.best.DogResnet50.keras')

### TODO: Write your algorithm.
### Feel free to use as many code cells as needed.

import keras
from keras.preprocessing import image                  
from keras.applications.resnet50 import ResNet50, preprocess_input, decode_predictions
from tqdm import tqdm
import cv2                
import matplotlib.pyplot as plt 
import numpy as np

class DogBreedClassifier():
    '''
    Detect and classify dog breeds from images.
    
    Attributes
    ----------
    model: keras.models.Sequential
        CNN model for classifying dog breeds - uses ResNet50 bottleneck features.
    
    Methods
    -------
    classify(image_path)
        if a dog is detected in the image, return the predicted breed;
        if a human is detected in the image, return the resembling dog breed;
        if neither is detected in the image, provide output that indicates an error.
    
    '''    
    
    def __init__(self):
        self.model = keras.models.load_model('saved_models/DogResnet50_model')
        self.face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')
        self.ResNet50_model = ResNet50(weights='imagenet')
        self.dog_names = dog_names = [item[20:-1] for item in sorted(glob("../../../data/dog_images/train/*/"))]
        
    def __str__(self):
        return f'{self.model}'

    def __path_to_tensor(self, img_path):
        '''
        Takes a string-valued file path to a color image as input and returns a 4D tensor.
        
        INPUT:
        img_path (str) path to image
        
        RETURNS:
        tensor (numpy.ndarray) 4D image tensor
        '''
        # loads RGB image as PIL.Image.Image type
        img = image.load_img(img_path, target_size=(224, 224))
        # convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)
        x = image.img_to_array(img)
        # convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor
        tensor = np.expand_dims(x, axis=0) 
        return tensor

    def __extract_Resnet50(self, tensor):
        '''
        Takes a tensor and extract Resnet50 bottleneck features.
        
        INPUT:
        tensor (numpy.ndarray) 4D image tensor.
        
        RETURNS:
        (numpy.ndarray) array of extracted bottleneck features for Resnet50        
        '''
        return ResNet50(weights='imagenet', include_top=False).predict(preprocess_input(tensor))
        
    def __face_detector(self, img_path):
        '''
        Takes a path to image file, loads the image and returns True if any human face is detected.
        
        INPUT:
        img_path (str) path to image file.
        
        RETURNS:
        (bool) True if any face is detected, False otherwise.        
        '''
        img = cv2.imread(img_path)
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        faces = self.face_cascade.detectMultiScale(gray)        
        return len(faces) > 0
    
    def __ResNet50_predict_labels(self, img_path):
        '''
        Takes a path to image file, preprocesses input, uses Resnet50 to classify the image and returns Resnet50 label with maximum probability.
        
        INPUT:
        img_path (str) path to image file.
        
        RETURNS:
        (int) label with maximum probability as classified by Resnet50_model.predict().        
        '''
        # returns prediction vector for image located at img_path
        img = preprocess_input(self.__path_to_tensor(img_path))
        return np.argmax(self.ResNet50_model.predict(img))

    def __dog_detector(self, img_path):
        '''
        Takes path to image file and returns True if a dog is detected.
        
        INPUT:
        img_path (str) path to image file.
        
        RETURNS:
        (bool) True if any dog is detected and False otherwise.
        '''
        prediction = self.__ResNet50_predict_labels(img_path)
        return ((prediction <= 268) & (prediction >= 151))
    
    
    def __DogResNet50_predict_breed(self, img_path):
        '''
        Takes path to image with a dog and human face and determines dog breed.
        
        INPUT:
        img_path (str) path to image file.
        
        RETURNS:
        dod_breed (str) name of a dog breed.
        '''
        # extract bottleneck features
        bottleneck_feature = self.__extract_Resnet50(self.__path_to_tensor(img_path))
        # obtain predicted vector
        predicted_vector = self.model.predict(bottleneck_feature)
        # return dog breed that is predicted by the model
        dog_breed = self.dog_names[np.argmax(predicted_vector)][15:].lower().replace("_"," ")
        return dog_breed

    def classify(self, img_path):
        '''
        Takes path to image file, determines if there is a dog or human face, prints the image and the dog breed (or dog breed thar most resembles human face).
        
        INPUT:
        img_path (str) path to image file.
        '''
        # print picture
        # print image
        
        img = cv2.imread(img_path)
        # convert BGR image to RGB for plotting
        cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

        # display the image
        plt.imshow(cv_rgb)
        plt.show()
        
        # checks if there is a dog or human face in the image
        dog_detected = self.__dog_detector(img_path)
        face_detected = self.__face_detector(img_path)
        if (dog_detected or face_detected) == 0:
            return "ERROR: no dog nor human face found - aborting..." 
        if dog_detected:
            print("found dog in picture!")        
        if face_detected:
            print("found human face in picture!")       
        
        breed = self.__DogResNet50_predict_breed(img_path)
        
        if dog_detected:
            print(f"dog in image is a {breed}")
        if face_detected:
            print(f"human in picture resembles a {breed}")

Test the Model

Now, we can use the CNN to test how well it identifies breed within our test dataset of dog images. We print the test accuracy below.

(IMPLEMENTATION) Predict Dog Breed with the Model

Write a function that takes an image path as input and returns the dog breed (Affenpinscher, Afghan_hound, etc) that is predicted by your model.

Similar to the analogous function in Step 5, your function should have three steps:

1. Extract the bottleneck features corresponding to the chosen CNN model.
2. Supply the bottleneck features as input to the model to return the predicted vector. Note that the argmax of this prediction vector gives the index of the predicted dog breed.
3. Use the dog_names array defined in Step 0 of this notebook to return the corresponding breed.

The functions to extract the bottleneck features can be found in extract_bottleneck_features.py, and they have been imported in an earlier code cell. To obtain the bottleneck features corresponding to your chosen CNN architecture, you need to use the function

extract_{network}

where {network}, in the above filename, should be one of VGG19, Resnet50, InceptionV3, or Xception.

### TODO: Write a function that takes a path to an image as input
### and returns the dog breed that is predicted by the model.

def extract_Resnet50(tensor):
    from tensorflow.keras.applications.resnet50 import ResNet50
    base_model = ResNet50(weights='imagenet', include_top=False)
    bottleneck_feature = base_model.predict(preprocess_input(tensor))
    return bottleneck_feature

def DogResNet50_predict_breed(img_path):
    # extract bottleneck features
    bottleneck_feature = extract_Resnet50(path_to_tensor(img_path))
    # obtain predicted vector
    predicted_vector = DogResnet50_model.predict(bottleneck_feature)
    # return dog breed that is predicted by the model
    # dog_breed = dog_names[np.argmax(predicted_vector)][15:].lower().replace("_"," ")
    return dog_names[np.argmax(predicted_vector)]

DogResNet50_predict_breed("images/sample_dog_output.png")

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'n\\078.Great_dane'

---

Step 6: Write your Algorithm

Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither. Then,

• if a dog is detected in the image, return the predicted breed.
• if a human is detected in the image, return the resembling dog breed.
• if neither is detected in the image, provide output that indicates an error.

You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the face_detector and dog_detector functions developed above. You are required to use your CNN from Step 5 to predict dog breed.

A sample image and output for our algorithm is provided below, but feel free to design your own user experience!

This photo looks like an Afghan Hound.

(IMPLEMENTATION) Write your Algorithm

---

Step 7: Test Your Algorithm

In this section, you will take your new algorithm for a spin! What kind of dog does the algorithm think that you look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog?

(IMPLEMENTATION) Test Your Algorithm on Sample Images!

Test your algorithm at least six images on your computer. Feel free to use any images you like. Use at least two human and two dog images.

Question 6: Is the output better than you expected :) ? Or worse :( ? Provide at least three possible points of improvement for your algorithm.

Answer: The output correctly identified breeds for one of my dogs, and for images provided by the project. It performed better than i imagined - and provided fun comparison with human faces.

For improving the algorithm, i would suggest:

• Including more dog images and more dog breeds - one of my dogs was incorrectly labled beacuse it's breed was not in the dataset.
• Using a more robust pre-trained CNN.
• Cropping the image around the dog or human face of the provided image, then performing the classification might yield better results.

import importlib

# Assuming your class is in a file named dog_breed_classifier.py
import DogBreedClassifier

# Reload the module to get the latest changes
importlib.reload(DogBreedClassifier)

# Now, you can use your updated DogBreedClassifier
breed_classifier = DogBreedClassifier.DogBreedClassifier()

# image 1: dog
# wrong, it's a poodle

breed_classifier.classify("images/baby_totoro.jpg")

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found dog in picture!
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dog in image is a .bichon frise

# image 2: dog
# dog is actualy a pinscher, but it does kinda look like a manchester terrier

breed_classifier.classify("images/caramela.jpeg")

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found dog in picture!
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dog in image is a manchester terrier

# image 3: human
# algorithm thinks i look like a lowchen... 

breed_classifier.classify("images/selfie.jpeg")

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found human face in picture!
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human in picture resembles a intli

# image 4: human
# john lennon looks like a lhasa apso

breed_classifier.classify("images/john.jpg")

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found human face in picture!
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human in picture resembles a  terrier

# image 5: wasabi
# image of random object, no dogs or humans identified

breed_classifier.classify("images/wasabi.jpg")

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'ERROR: no dog nor human face found - aborting...'

# image 6: dog
# correctly identified!

breed_classifier.classify("images/Labrador_retriever_06449.jpg")

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found dog in picture!
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dog in image is a retriever

# image 7: dog
# correctly identified!

breed_classifier.classify("images/Welsh_springer_spaniel_08203.jpg")

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found dog in picture!
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dog in image is a inger spaniel

Ipoiting the class from DogBreedClassifier.py

Just import the class and use the method as follows

from DogBreedClassifier import DogBreedClassifier

classifier = DogBreedClassifier()
classifier.classify("images/Welsh_springer_spaniel_08203.jpg")

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found dog in picture!
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dog in image is a inger spaniel

breed_classifier.classify("images/sh2.jpg")

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found dog in picture!
found human face in picture!
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dog in image is a alaskan malamute
human in picture resembles a alaskan malamute